{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0c11fa43",
   "metadata": {},
   "source": [
    "数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a1b33e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下载中文停用词表...\n",
      "成功加载 794 个停用词\n",
      "正在读取数据并进行字符级分词...\n",
      "训练集大小: 200000\n",
      "测试集大小: 19999\n",
      "验证集大小: 19991\n",
      "\n",
      "训练集类别分布:\n",
      "label\n",
      "体育    20000\n",
      "娱乐    20000\n",
      "家居    20000\n",
      "教育    20000\n",
      "时政    20000\n",
      "游戏    20000\n",
      "社会    20000\n",
      "科技    20000\n",
      "股票    20000\n",
      "财经    20000\n",
      "Name: count, dtype: int64\n",
      "\n",
      "字符级分词后的数据样例:\n",
      "               original_text                                             text  \\\n",
      "0  国字号主帅年收入将达50万 有望直接签到2014年  国 字 号 主 帅 年 收 入 将 达 5 0 万 有 望 直 接 签 到 2 0 1 4 年   \n",
      "1   姚明退役令易建联震惊 7月18日正式和国家队汇合    姚 明 退 役 令 易 建 联 震 惊 7 月 1 8 日 正 式 和 国 家 队 汇 合   \n",
      "2   国青小将染红或遭洋帅清洗 后防核心无缘亚青赛首战    国 青 小 将 染 红 或 遭 洋 帅 清 洗 后 防 核 心 无 缘 亚 青 赛 首 战   \n",
      "3    七年一轮回再碰马刺 巴蒂尔：离开火箭至今难释怀        七 年 一 轮 回 再 碰 马 刺 巴 蒂 尔 离 开 火 箭 至 今 难 释 怀   \n",
      "4   小布脚踝扭伤进观察名单 西部第8之争休城先输一手    小 布 脚 踝 扭 伤 进 观 察 名 单 西 部 第 8 之 争 休 城 先 输 一 手   \n",
      "\n",
      "  label  \n",
      "0    体育  \n",
      "1    体育  \n",
      "2    体育  \n",
      "3    体育  \n",
      "4    体育  \n",
      "\n",
      "标签编码映射:\n",
      "体育 -> 0\n",
      "娱乐 -> 1\n",
      "家居 -> 2\n",
      "教育 -> 3\n",
      "时政 -> 4\n",
      "游戏 -> 5\n",
      "社会 -> 6\n",
      "科技 -> 7\n",
      "股票 -> 8\n",
      "财经 -> 9\n",
      "\n",
      "开始TF-IDF特征提取...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:551: UserWarning: The parameter 'token_pattern' will not be used since 'analyzer' != 'word'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF特征维度: 10000\n",
      "训练集样本数: 200000\n",
      "测试集样本数: 19999\n",
      "验证集样本数: 19991\n",
      "训练集稀疏度: 0.006971\n",
      "\n",
      "TF-IDF特征示例:\n",
      " !\n",
      " ! \n",
      " %\n",
      " % \n",
      " &\n",
      " & \n",
      " (\n",
      " ( \n",
      " )\n",
      " ) \n",
      "\n",
      "执行PCA降维...\n",
      "PCA降维后特征维度: 200\n",
      "解释方差比例: 0.3342\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32047 (\\N{CJK UNIFIED IDEOGRAPH-7D2F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31215 (\\N{CJK UNIFIED IDEOGRAPH-79EF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35299 (\\N{CJK UNIFIED IDEOGRAPH-89E3}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37322 (\\N{CJK UNIFIED IDEOGRAPH-91CA}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26041 (\\N{CJK UNIFIED IDEOGRAPH-65B9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24046 (\\N{CJK UNIFIED IDEOGRAPH-5DEE}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20363 (\\N{CJK UNIFIED IDEOGRAPH-4F8B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29305 (\\N{CJK UNIFIED IDEOGRAPH-7279}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24449 (\\N{CJK UNIFIED IDEOGRAPH-5F81}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20027 (\\N{CJK UNIFIED IDEOGRAPH-4E3B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25104 (\\N{CJK UNIFIED IDEOGRAPH-6210}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38477 (\\N{CJK UNIFIED IDEOGRAPH-964D}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32500 (\\N{CJK UNIFIED IDEOGRAPH-7EF4}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21518 (\\N{CJK UNIFIED IDEOGRAPH-540E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25454 (\\N{CJK UNIFIED IDEOGRAPH-636E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24067 (\\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import io\n",
    "import jieba\n",
    "\n",
    "# 设置数据路径\n",
    "data_dir = r'C:\\Users\\32107\\Desktop\\shit-code\\data\\THUCNews-data'\n",
    "train_path = os.path.join(data_dir, 'train.txt')\n",
    "test_path = os.path.join(data_dir, 'test.txt')\n",
    "dev_path = os.path.join(data_dir, 'dev.txt')\n",
    "\n",
    "# 下载停用词表\n",
    "print(\"下载中文停用词表...\")\n",
    "stopwords_url = \"https://raw.githubusercontent.com/stopwords-iso/stopwords-zh/master/stopwords-zh.txt\"\n",
    "response = requests.get(stopwords_url)\n",
    "stopwords = set([line.strip() for line in io.StringIO(response.text.strip())])\n",
    "print(f\"成功加载 {len(stopwords)} 个停用词\")\n",
    "\n",
    "# 字符级分词函数\n",
    "# 修改字符级分词函数\n",
    "def char_tokenize(text):\n",
    "    # 将文本按字符分割，但只过滤掉空格，不过滤所有停用词\n",
    "    # 对于中文字符级处理，大部分单字都是有意义的\n",
    "    chars = []\n",
    "    for char in text:\n",
    "        if char.strip():  # 只过滤空白字符\n",
    "            # 只过滤常见的标点和特殊符号，不过滤所有停用词\n",
    "            if char not in '，。！？；：\"\"''（）【】、':\n",
    "                chars.append(char)\n",
    "    return chars\n",
    "\n",
    "# 重新定义读取数据的函数\n",
    "def read_data(file_path):\n",
    "    texts = []\n",
    "    tokenized_texts = []\n",
    "    labels = []\n",
    "    with open(file_path, 'r', encoding='utf-8') as f:\n",
    "        for line in f:\n",
    "            try:\n",
    "                line = line.strip()\n",
    "                if not line:\n",
    "                    continue\n",
    "                # THUCNews格式通常是标签和文本以\\t分隔\n",
    "                parts = line.split('\\t')\n",
    "                if len(parts) >= 2:\n",
    "                    label = parts[0]\n",
    "                    text = parts[1]\n",
    "                    # 字符级分词\n",
    "                    tokenized_text = char_tokenize(text)\n",
    "                    # 将分词结果重新连接成字符串，用空格分隔\n",
    "                    tokenized_text_str = ' '.join(tokenized_text)\n",
    "                    \n",
    "                    # 确保分词后的文本不为空\n",
    "                    if tokenized_text_str.strip():\n",
    "                        texts.append(text)\n",
    "                        tokenized_texts.append(tokenized_text_str)\n",
    "                        labels.append(label)\n",
    "                    else:\n",
    "                        print(f\"警告: 跳过空文本，原始文本: {text[:50]}...\")\n",
    "            except Exception as e:\n",
    "                print(f\"Error processing line: {line}, error: {e}\")\n",
    "    return pd.DataFrame({'original_text': texts, 'text': tokenized_texts, 'label': labels})\n",
    "\n",
    "# 读取数据\n",
    "print(\"正在读取数据并进行字符级分词...\")\n",
    "train_df = read_data(train_path)\n",
    "test_df = read_data(test_path)\n",
    "dev_df = read_data(dev_path)\n",
    "\n",
    "# 显示数据基本信息\n",
    "print(f\"训练集大小: {len(train_df)}\")\n",
    "print(f\"测试集大小: {len(test_df)}\")\n",
    "print(f\"验证集大小: {len(dev_df)}\")\n",
    "\n",
    "# 查看类别分布\n",
    "print(\"\\n训练集类别分布:\")\n",
    "print(train_df['label'].value_counts())\n",
    "\n",
    "# 显示处理后的数据样例\n",
    "print(\"\\n字符级分词后的数据样例:\")\n",
    "print(train_df[['original_text', 'text', 'label']].head())\n",
    "\n",
    "# 标签编码和TF-IDF特征提取\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "\n",
    "# 标签编码\n",
    "label_encoder = LabelEncoder()\n",
    "train_labels_encoded = label_encoder.fit_transform(train_df['label'])\n",
    "test_labels_encoded = label_encoder.transform(test_df['label'])\n",
    "dev_labels_encoded = label_encoder.transform(dev_df['label'])\n",
    "\n",
    "print(\"\\n标签编码映射:\")\n",
    "for i, label in enumerate(label_encoder.classes_):\n",
    "    print(f\"{label} -> {i}\")\n",
    "\n",
    "# 修改TF-IDF向量化器部分\n",
    "print(\"\\n开始TF-IDF特征提取...\")\n",
    "tfidf_vectorizer = TfidfVectorizer(\n",
    "    max_features=10000,   # 最多保留的特征数\n",
    "    min_df=2,             # 降低最小文档频率要求\n",
    "    max_df=0.95,          # 提高最大文档频率阈值\n",
    "    ngram_range=(1, 3),   # 考虑1-3gram特征\n",
    "    analyzer='char',      # 使用字符级分析而不是词级\n",
    "    token_pattern=r'(?u)\\b\\w+\\b|.'  # 允许单个字符作为特征\n",
    ")\n",
    "\n",
    "# 在训练集上拟合并转换\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(train_df['text'])\n",
    "X_test_tfidf = tfidf_vectorizer.transform(test_df['text'])\n",
    "X_dev_tfidf = tfidf_vectorizer.transform(dev_df['text'])\n",
    "\n",
    "# 打印特征维度信息\n",
    "print(f\"TF-IDF特征维度: {X_train_tfidf.shape[1]}\")\n",
    "print(f\"训练集样本数: {X_train_tfidf.shape[0]}\")\n",
    "print(f\"测试集样本数: {X_test_tfidf.shape[0]}\")\n",
    "print(f\"验证集样本数: {X_dev_tfidf.shape[0]}\")\n",
    "\n",
    "# 查看特征稀疏度\n",
    "print(f\"训练集稀疏度: {X_train_tfidf.nnz / (X_train_tfidf.shape[0] * X_train_tfidf.shape[1]):.6f}\")\n",
    "\n",
    "# 获取最重要的特征\n",
    "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
    "print(\"\\nTF-IDF特征示例:\")\n",
    "for i in range(min(10, len(feature_names))):\n",
    "    print(feature_names[i])\n",
    "\n",
    "# 使用PCA或TruncatedSVD降维(对于稀疏矩阵，使用TruncatedSVD)\n",
    "print(\"\\n执行PCA降维...\")\n",
    "n_components = 200  # 降维后的特征数量\n",
    "svd = TruncatedSVD(n_components=n_components, random_state=42)\n",
    "X_train_pca = svd.fit_transform(X_train_tfidf)\n",
    "X_test_pca = svd.transform(X_test_tfidf)\n",
    "X_dev_pca = svd.transform(X_dev_tfidf)\n",
    "\n",
    "# 打印降维信息\n",
    "print(f\"PCA降维后特征维度: {X_train_pca.shape[1]}\")\n",
    "print(f\"解释方差比例: {np.sum(svd.explained_variance_ratio_):.4f}\")\n",
    "\n",
    "# 可视化累积解释方差\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(np.cumsum(svd.explained_variance_ratio_))\n",
    "plt.xlabel('PCA特征数量')\n",
    "plt.ylabel('累积解释方差比例')\n",
    "plt.title('PCA累积解释方差')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 显示一些样本在降维后空间中的分布\n",
    "plt.figure(figsize=(12, 10))\n",
    "colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'orange', 'purple', 'brown']\n",
    "markers = ['o', 's', '^', 'v', '<', '>', 'p', '*', 'h', 'H']\n",
    "\n",
    "# 选择部分类别和样本以便可视化\n",
    "sample_size = 300\n",
    "selected_indices = []\n",
    "for label in range(len(label_encoder.classes_)):\n",
    "    indices = np.where(train_labels_encoded == label)[0]\n",
    "    if len(indices) > 0:\n",
    "        selected_indices.extend(np.random.choice(indices, min(30, len(indices)), replace=False))\n",
    "\n",
    "# 绘制散点图\n",
    "for i, label in enumerate(label_encoder.classes_):\n",
    "    idx = np.where(train_labels_encoded[selected_indices] == i)[0]\n",
    "    if len(idx) > 0:\n",
    "        plt.scatter(\n",
    "            X_train_pca[selected_indices][idx, 0], \n",
    "            X_train_pca[selected_indices][idx, 1],\n",
    "            c=colors[i % len(colors)], \n",
    "            marker=markers[i % len(markers)],\n",
    "            label=label,\n",
    "            alpha=0.7\n",
    "        )\n",
    "\n",
    "plt.legend(loc='best')\n",
    "plt.title('PCA降维后的数据分布')\n",
    "plt.xlabel('主成分1')\n",
    "plt.ylabel('主成分2')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fffcc7c6",
   "metadata": {},
   "source": [
    "Logistic模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5faaebbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练Logistic回归模型...\n",
      "开始训练Logistic回归模型...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "convergence after 16 epochs took 34 seconds\n",
      "Logistic回归模型训练时间: 34.27 秒\n",
      "Logistic回归模型预测时间: 0.01 秒\n",
      "\n",
      "Logistic回归测试集准确率: 0.8319\n",
      "\n",
      "Logistic回归详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.85      0.88      0.87      1999\n",
      "          娱乐       0.79      0.79      0.79      2000\n",
      "          家居       0.79      0.82      0.80      2000\n",
      "          教育       0.91      0.89      0.90      2000\n",
      "          时政       0.75      0.78      0.77      2000\n",
      "          游戏       0.85      0.84      0.85      2000\n",
      "          社会       0.84      0.84      0.84      2000\n",
      "          科技       0.89      0.85      0.87      2000\n",
      "          股票       0.79      0.79      0.79      2000\n",
      "          财经       0.86      0.82      0.84      2000\n",
      "\n",
      "    accuracy                           0.83     19999\n",
      "   macro avg       0.83      0.83      0.83     19999\n",
      "weighted avg       0.83      0.83      0.83     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "\n",
    "print(\"\\n训练Logistic回归模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建逻辑回归模型\n",
    "# 使用saga求解器以更有效地处理大规模稀疏数据\n",
    "logistic_model = LogisticRegression(\n",
    "    C=1.0,                # 正则化强度的倒数\n",
    "    solver='saga',        # 适用于大型稀疏数据集的solver\n",
    "    max_iter=200,         # 增加最大迭代次数以确保收敛\n",
    "    n_jobs=-1,            # 使用所有CPU核心\n",
    "    random_state=42,      # 随机种子\n",
    "    multi_class='multinomial',  # 多分类策略\n",
    "    class_weight='balanced',    # 处理类别不平衡问题\n",
    "    verbose=1             # 显示训练进度\n",
    ")\n",
    "\n",
    "# 在降维后的训练集上训练模型\n",
    "print(\"开始训练Logistic回归模型...\")\n",
    "# 使用PCA降维后的特征训练模型\n",
    "logistic_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "log_training_time = time.time() - start_time\n",
    "print(f\"Logistic回归模型训练时间: {log_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = logistic_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"Logistic回归模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "log_accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print(f\"\\nLogistic回归测试集准确率: {log_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\nLogistic回归详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "report = classification_report(test_labels_encoded, y_pred, target_names=class_names)\n",
    "print(report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "conf_matrix = confusion_matrix(test_labels_encoded, y_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Logistic Regression Confusion Matrix')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "506dda08",
   "metadata": {},
   "source": [
    "SVM模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "371c8b65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练SVM模型...\n",
      "开始训练SVM模型，这可能需要一些时间...\n",
      "[LibLinear][LibLinear][LibLinear]SVM模型训练时间: 67.82 秒\n",
      "SVM模型预测时间: 0.07 秒\n",
      "\n",
      "SVM测试集准确率: 0.8254\n",
      "\n",
      "SVM详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.83      0.89      0.86      1999\n",
      "          娱乐       0.79      0.77      0.78      2000\n",
      "          家居       0.78      0.82      0.80      2000\n",
      "          教育       0.90      0.89      0.90      2000\n",
      "          时政       0.75      0.77      0.76      2000\n",
      "          游戏       0.85      0.83      0.84      2000\n",
      "          社会       0.83      0.85      0.84      2000\n",
      "          科技       0.88      0.85      0.86      2000\n",
      "          股票       0.79      0.78      0.79      2000\n",
      "          财经       0.85      0.81      0.83      2000\n",
      "\n",
      "    accuracy                           0.83     19999\n",
      "   macro avg       0.83      0.83      0.83     19999\n",
      "weighted avg       0.83      0.83      0.83     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\3396970396.py:75: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "print(\"\\n训练SVM模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建SVM模型\n",
    "# 使用LinearSVC（线性SVM）因为它对大规模数据效率更高\n",
    "# 为了得到概率输出，使用CalibratedClassifierCV包装\n",
    "base_svm = LinearSVC(\n",
    "    C=1.0,               # 正则化参数\n",
    "    dual=False,          # 对于样本数 > 特征数的情况，设为False更高效\n",
    "    max_iter=1000,       # 增加迭代次数确保收敛\n",
    "    random_state=42,     # 随机种子\n",
    "    class_weight='balanced',  # 平衡类别权重，处理类别不平衡问题\n",
    "    verbose=1            # 显示训练进度\n",
    ")\n",
    "\n",
    "# 包装为校准分类器以获取概率输出\n",
    "svm_model = CalibratedClassifierCV(\n",
    "    estimator=base_svm,\n",
    "    cv=3,                # 交叉验证折数\n",
    "    method='sigmoid'     # 使用Platt缩放\n",
    ")\n",
    "\n",
    "# 在降维后的训练集上训练模型\n",
    "print(\"开始训练SVM模型，这可能需要一些时间...\")\n",
    "# 使用PCA降维后的特征训练模型\n",
    "svm_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "svm_training_time = time.time() - start_time\n",
    "print(f\"SVM模型训练时间: {svm_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "svm_pred = svm_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"SVM模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "svm_accuracy = accuracy_score(test_labels_encoded, svm_pred)\n",
    "print(f\"\\nSVM测试集准确率: {svm_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\nSVM详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "svm_report = classification_report(test_labels_encoded, svm_pred, target_names=class_names)\n",
    "print(svm_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "svm_conf_matrix = confusion_matrix(test_labels_encoded, svm_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(svm_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('SVM模型混淆矩阵')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d44e674",
   "metadata": {},
   "source": [
    "随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89846e0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练随机森林模型...\n",
      "开始训练随机森林模型，这可能需要一些时间...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   40.8s finished\n",
      "[Parallel(n_jobs=32)]: Using backend ThreadingBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Done 100 out of 100 | elapsed:    0.0s finished\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林模型训练时间: 40.99 秒\n",
      "随机森林模型预测时间: 0.12 秒\n",
      "\n",
      "随机森林测试集准确率: 0.7944\n",
      "\n",
      "随机森林详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.76      0.85      0.80      1999\n",
      "          娱乐       0.74      0.71      0.72      2000\n",
      "          家居       0.78      0.79      0.78      2000\n",
      "          教育       0.87      0.87      0.87      2000\n",
      "          时政       0.68      0.73      0.70      2000\n",
      "          游戏       0.81      0.82      0.82      2000\n",
      "          社会       0.78      0.82      0.80      2000\n",
      "          科技       0.90      0.81      0.85      2000\n",
      "          股票       0.76      0.78      0.77      2000\n",
      "          财经       0.90      0.77      0.83      2000\n",
      "\n",
      "    accuracy                           0.79     19999\n",
      "   macro avg       0.80      0.79      0.80     19999\n",
      "weighted avg       0.80      0.79      0.80     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 38543 (\\N{CJK UNIFIED IDEOGRAPH-968F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 26426 (\\N{CJK UNIFIED IDEOGRAPH-673A}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 26862 (\\N{CJK UNIFIED IDEOGRAPH-68EE}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 26519 (\\N{CJK UNIFIED IDEOGRAPH-6797}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\636148877.py:68: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38543 (\\N{CJK UNIFIED IDEOGRAPH-968F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26426 (\\N{CJK UNIFIED IDEOGRAPH-673A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26862 (\\N{CJK UNIFIED IDEOGRAPH-68EE}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26519 (\\N{CJK UNIFIED IDEOGRAPH-6797}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"\\n训练随机森林模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建随机森林模型\n",
    "# 注意：随机森林对于大规模稀疏特征可能较慢，需要调整参数\n",
    "rf_model = RandomForestClassifier(\n",
    "    n_estimators=100,     # 树的数量\n",
    "    max_depth=None,       # 树的最大深度，None表示不限制\n",
    "    min_samples_split=2,  # 分裂内部节点所需的最小样本数\n",
    "    min_samples_leaf=1,   # 叶节点所需的最小样本数\n",
    "    max_features='sqrt',  # 寻找最佳分割时考虑的特征数量\n",
    "    bootstrap=True,       # 是否使用bootstrap抽样\n",
    "    n_jobs=-1,            # 使用所有CPU核心\n",
    "    random_state=42,      # 随机种子\n",
    "    class_weight='balanced',  # 平衡类别权重\n",
    "    verbose=1             # 显示训练进度\n",
    ")\n",
    "\n",
    "# 在PCA降维后的训练集上训练模型\n",
    "print(\"开始训练随机森林模型，这可能需要一些时间...\")\n",
    "rf_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "rf_training_time = time.time() - start_time\n",
    "print(f\"随机森林模型训练时间: {rf_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "rf_pred = rf_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"随机森林模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "rf_accuracy = accuracy_score(test_labels_encoded, rf_pred)\n",
    "print(f\"\\n随机森林测试集准确率: {rf_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\n随机森林详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "rf_report = classification_report(test_labels_encoded, rf_pred, target_names=class_names)\n",
    "print(rf_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "rf_conf_matrix = confusion_matrix(test_labels_encoded, rf_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(rf_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('随机森林模型混淆矩阵')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71631962",
   "metadata": {},
   "source": [
    "决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29f8ccab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练决策树模型...\n",
      "开始训练决策树模型...\n",
      "决策树模型训练时间: 9.17 秒\n",
      "决策树模型预测时间: 0.01 秒\n",
      "\n",
      "决策树测试集准确率: 0.6053\n",
      "\n",
      "决策树详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.56      0.65      0.61      1999\n",
      "          娱乐       0.47      0.54      0.50      2000\n",
      "          家居       0.54      0.57      0.56      2000\n",
      "          教育       0.75      0.70      0.72      2000\n",
      "          时政       0.47      0.50      0.49      2000\n",
      "          游戏       0.66      0.65      0.65      2000\n",
      "          社会       0.65      0.63      0.64      2000\n",
      "          科技       0.74      0.65      0.69      2000\n",
      "          股票       0.59      0.55      0.57      2000\n",
      "          财经       0.71      0.61      0.66      2000\n",
      "\n",
      "    accuracy                           0.61     19999\n",
      "   macro avg       0.61      0.61      0.61     19999\n",
      "weighted avg       0.61      0.61      0.61     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 20915 (\\N{CJK UNIFIED IDEOGRAPH-51B3}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 31574 (\\N{CJK UNIFIED IDEOGRAPH-7B56}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 26641 (\\N{CJK UNIFIED IDEOGRAPH-6811}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\2787720315.py:64: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26631 (\\N{CJK UNIFIED IDEOGRAPH-6807}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31614 (\\N{CJK UNIFIED IDEOGRAPH-7B7E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20915 (\\N{CJK UNIFIED IDEOGRAPH-51B3}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31574 (\\N{CJK UNIFIED IDEOGRAPH-7B56}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26641 (\\N{CJK UNIFIED IDEOGRAPH-6811}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"\\n训练决策树模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建决策树模型\n",
    "dt_model = DecisionTreeClassifier(\n",
    "    max_depth=20,           # 限制树的最大深度，防止过拟合\n",
    "    min_samples_split=5,    # 分裂内部节点所需的最小样本数\n",
    "    min_samples_leaf=2,     # 叶节点所需的最小样本数\n",
    "    max_features='sqrt',    # 寻找最佳分割时考虑的特征数量\n",
    "    random_state=42,        # 随机种子\n",
    "    class_weight='balanced'  # 平衡类别权重\n",
    ")\n",
    "\n",
    "# 在降维后的训练集上训练模型\n",
    "print(\"开始训练决策树模型...\")\n",
    "dt_model.fit(X_train_pca, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "dt_training_time = time.time() - start_time\n",
    "print(f\"决策树模型训练时间: {dt_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "dt_pred = dt_model.predict(X_test_pca)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"决策树模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "dt_accuracy = accuracy_score(test_labels_encoded, dt_pred)\n",
    "print(f\"\\n决策树测试集准确率: {dt_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\n决策树详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "dt_report = classification_report(test_labels_encoded, dt_pred, target_names=class_names)\n",
    "print(dt_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "dt_conf_matrix = confusion_matrix(test_labels_encoded, dt_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(dt_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.title('决策树模型混淆矩阵')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851656b6",
   "metadata": {},
   "source": [
    "朴素贝叶斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "897fb3fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练朴素贝叶斯模型...\n",
      "开始训练朴素贝叶斯模型...\n",
      "朴素贝叶斯模型训练时间: 0.11 秒\n",
      "朴素贝叶斯模型预测时间: 0.01 秒\n",
      "\n",
      "朴素贝叶斯测试集准确率: 0.8659\n",
      "\n",
      "朴素贝叶斯详细分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          体育       0.88      0.93      0.90      1999\n",
      "          娱乐       0.81      0.89      0.85      2000\n",
      "          家居       0.85      0.86      0.86      2000\n",
      "          教育       0.92      0.88      0.90      2000\n",
      "          时政       0.80      0.84      0.82      2000\n",
      "          游戏       0.86      0.88      0.87      2000\n",
      "          社会       0.87      0.88      0.88      2000\n",
      "          科技       0.96      0.81      0.88      2000\n",
      "          股票       0.85      0.82      0.83      2000\n",
      "          财经       0.88      0.86      0.87      2000\n",
      "\n",
      "    accuracy                           0.87     19999\n",
      "   macro avg       0.87      0.87      0.87     19999\n",
      "weighted avg       0.87      0.87      0.87     19999\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.draw()\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32946 (\\N{CJK UNIFIED IDEOGRAPH-80B2}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23089 (\\N{CJK UNIFIED IDEOGRAPH-5A31}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20048 (\\N{CJK UNIFIED IDEOGRAPH-4E50}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23478 (\\N{CJK UNIFIED IDEOGRAPH-5BB6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23621 (\\N{CJK UNIFIED IDEOGRAPH-5C45}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25945 (\\N{CJK UNIFIED IDEOGRAPH-6559}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25919 (\\N{CJK UNIFIED IDEOGRAPH-653F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28216 (\\N{CJK UNIFIED IDEOGRAPH-6E38}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25103 (\\N{CJK UNIFIED IDEOGRAPH-620F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31038 (\\N{CJK UNIFIED IDEOGRAPH-793E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20250 (\\N{CJK UNIFIED IDEOGRAPH-4F1A}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31185 (\\N{CJK UNIFIED IDEOGRAPH-79D1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32929 (\\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31080 (\\N{CJK UNIFIED IDEOGRAPH-7968}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 36130 (\\N{CJK UNIFIED IDEOGRAPH-8D22}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "c:\\Users\\32107\\anaconda3\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32463 (\\N{CJK UNIFIED IDEOGRAPH-7ECF}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import time\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"\\n训练朴素贝叶斯模型...\")\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 创建多项式朴素贝叶斯模型 - 适合处理词频特征\n",
    "nb_model = MultinomialNB(\n",
    "    alpha=0.1,  # 平滑参数，较小的值意味着较少的平滑\n",
    "    fit_prior=True  # 是否学习类别先验概率\n",
    ")\n",
    "\n",
    "# 在训练集上训练模型\n",
    "print(\"开始训练朴素贝叶斯模型...\")\n",
    "nb_model.fit(X_train_tfidf, train_labels_encoded)\n",
    "\n",
    "# 计算训练时间\n",
    "nb_training_time = time.time() - start_time\n",
    "print(f\"朴素贝叶斯模型训练时间: {nb_training_time:.2f} 秒\")\n",
    "\n",
    "# 计时开始 - 预测时间\n",
    "predict_start_time = time.time()\n",
    "\n",
    "# 在测试集上进行预测\n",
    "nb_pred = nb_model.predict(X_test_tfidf)\n",
    "\n",
    "# 计算预测时间\n",
    "predict_time = time.time() - predict_start_time\n",
    "print(f\"朴素贝叶斯模型预测时间: {predict_time:.2f} 秒\")\n",
    "\n",
    "# 计算准确率\n",
    "nb_accuracy = accuracy_score(test_labels_encoded, nb_pred)\n",
    "print(f\"\\n朴素贝叶斯测试集准确率: {nb_accuracy:.4f}\")\n",
    "\n",
    "# 生成分类报告\n",
    "print(\"\\n朴素贝叶斯详细分类报告:\")\n",
    "class_names = label_encoder.classes_\n",
    "nb_report = classification_report(test_labels_encoded, nb_pred, target_names=class_names)\n",
    "print(nb_report)\n",
    "\n",
    "# 计算混淆矩阵\n",
    "nb_conf_matrix = confusion_matrix(test_labels_encoded, nb_pred)\n",
    "\n",
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(14, 12))\n",
    "sns.heatmap(nb_conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Naive Bayes Confusion Matrix')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c677477c",
   "metadata": {},
   "source": [
    "比较各模型的训练时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69ac4f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\32107\\AppData\\Local\\Temp\\ipykernel_12648\\1407822824.py:33: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax1.set_xticklabels(performance_df['Model'], rotation=45, ha='right')\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能对比:\n",
      "                 Model Training Time (s) Accuracy\n",
      "0  Logistic Regression             34.27   0.8319\n",
      "1                  SVM             67.82   0.8254\n",
      "2        Random Forest             40.99   0.7944\n",
      "3        Decision Tree              9.17   0.6053\n",
      "4          Naive Bayes              0.11   0.8659\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 收集所有模型的训练时间\n",
    "models = ['Logistic Regression', 'SVM', 'Random Forest', 'Decision Tree', 'Naive Bayes']\n",
    "training_times = [log_training_time, svm_training_time, rf_training_time, dt_training_time, nb_training_time]\n",
    "accuracies = [log_accuracy, svm_accuracy, rf_accuracy, dt_accuracy, nb_accuracy]\n",
    "# 创建DataFrame以便于绘图和分析\n",
    "performance_df = pd.DataFrame({\n",
    "    'Model': models,\n",
    "    'Training Time (s)': training_times,\n",
    "    'Accuracy': accuracies\n",
    "})\n",
    "# 创建一个包含训练时间和准确率的双轴图表\n",
    "fig, ax1 = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# 设置第一个Y轴（训练时间）\n",
    "color = 'tab:blue'\n",
    "ax1.set_xlabel('Model', fontsize=12)\n",
    "ax1.set_ylabel('Training Time (seconds)', color=color, fontsize=12)\n",
    "bars = ax1.bar(performance_df['Model'], performance_df['Training Time (s)'], color=color, alpha=0.7)\n",
    "\n",
    "# 添加训练时间数据标签\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2., height + 0.5,\n",
    "             f'{height:.2f}s', ha='center', va='bottom', fontsize=10, color=color)\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "ax1.set_xticklabels(performance_df['Model'], rotation=45, ha='right')\n",
    "\n",
    "# 创建第二个Y轴（准确率）\n",
    "ax2 = ax1.twinx()\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Accuracy', color=color, fontsize=12)\n",
    "ax2.plot(performance_df['Model'], performance_df['Accuracy'], 'o-', color=color, linewidth=2, markersize=8)\n",
    "\n",
    "# 添加准确率数据标签\n",
    "for i, acc in enumerate(performance_df['Accuracy']):\n",
    "    ax2.text(i, acc + 0.01, f'{acc:.4f}', ha='center', va='bottom', fontsize=10, color=color)\n",
    "\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.set_ylim([0, 1.05])  # 设置准确率轴的范围\n",
    "\n",
    "# 添加标题和网格\n",
    "plt.title('Model Performance: Training Time vs. Accuracy', fontsize=15)\n",
    "ax1.grid(axis='y', linestyle='--', alpha=0.3)\n",
    "fig.tight_layout()\n",
    "\n",
    "# 显示图表\n",
    "plt.show()\n",
    "# 打印性能对比表格\n",
    "print(\"\\n模型性能对比:\")\n",
    "performance_df['Accuracy'] = performance_df['Accuracy'].apply(lambda x: f\"{x:.4f}\")\n",
    "performance_df['Training Time (s)'] = performance_df['Training Time (s)'].apply(lambda x: f\"{x:.2f}\")\n",
    "print(performance_df)"
   ]
  }
 ],
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